There’s a problem with the deployment using deepstream 6.0 and jetson nano board in that the output layer in deepstream is different from the output layer in the jupyter notebook for the same input.
There is a similar shape, but different values in both, which causes a wrong output in deepstream.
I have edited the parser for CTC greedy decoding and tested it; I believe it works fine and the problem is in the config files, I have tried changing many config parameters but I couldn’t solve the problem.
[streammux]
gpu-id=0 #Boolean property to inform muxer that sources are live
live-source=0
batch-size=1 #time out in usec, to wait after the first buffer is available #to push the batch even if the complete batch is not formed
batched-push-timeout=40000
Set muxer output width and height
width=1080
height=720 enable to maintain aspect ratio wrt source, and allow black borders, works #along with width, height properties
enable-padding=0
nvbuf-memory-type=0
config-file property is mandatory for any gie section.
Other properties are optional and if set will override the properties set in
the infer config file.
[primary-gie]
enable=1
gpu-id=0
#model-engine-file=model_b1_int8.engine
labelfile-path=dict_us.txt
batch-size=1 #Required by the app for OSD, not a plugin property
bbox-border-color0=1;0;0;1
bbox-border-color1=0;1;1;1
bbox-border-color2=0;0;1;1
bbox-border-color3=0;1;0;1
#interval=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_file.txt
plus, I want to know why the output layer values changed, this is very important to me because I faced this problem in many models.
understanding this problem and solving it is very important to me, can you please help me to figure it out?
